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Difference between revisions of "The logic of the probabilistic language"
The logic of the probabilistic language (view source)
Revision as of 11:13, 23 June 2024
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<center> | <center> | ||
<big>'''Second Clinical Approach'''</big> | |||
''(hover over the images)'' | ''(hover over the images)'' | ||
<gallery widths="350" heights="282" perrow="2" mode="slideshow"> | <gallery widths="350" heights="282" perrow="2" mode="slideshow"> | ||
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File:Atm1 sclerodermia.jpg|'''<!--85-->Figure 3:''' Computed Tomography of the TMJ | File:Atm1 sclerodermia.jpg|'''<!--85-->Figure 3:''' Computed Tomography of the TMJ | ||
File:Spasmo emimasticatorio assiografia.jpg|'''<!--87-->Figure 4:''' Axiography of the patient showing a flattening of the chewing pattern on the right condyle | File:Spasmo emimasticatorio assiografia.jpg|'''<!--87-->Figure 4:''' Axiography of the patient showing a flattening of the chewing pattern on the right condyle | ||
File:EMG2.jpg|'''<!--89-->Figure 5:''' EMG Interferential Pattern. Overlapping upper traces corresponding to the right masseter, lower to the left masseter. | File:EMG2.jpg|'''<!--89-->Figure 5:''' EMG Interferential Pattern. Overlapping upper traces corresponding to the right masseter, lower to the left masseter. | ||
</gallery> | </gallery> | ||
</center> | </center> | ||
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In order to take advantage of the information provided by this dataset, the concept of partition of causal relevance is introduced: | In order to take advantage of the information provided by this dataset, the concept of partition of causal relevance is introduced: | ||
====The partition of causal relevance==== | ==== The partition of causal relevance==== | ||
:Always be <math>n</math> the number of people we have to conduct the analyses upon, if we divide (based on certain conditions as explained below) this group into <math>k</math> subsets <math>C_i</math> with <math>i=1,2,\dots,k</math>, a cluster is created that is called a "partition set" <math>\pi</math>: | :Always be <math>n</math> the number of people we have to conduct the analyses upon, if we divide (based on certain conditions as explained below) this group into <math>k</math> subsets <math>C_i</math> with <math>i=1,2,\dots,k</math>, a cluster is created that is called a "partition set" <math>\pi</math>: | ||
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|<math>P(D| noDeg.TMJ \cap noTMDs)=0.001 \qquad \qquad \;</math> | |<math>P(D| noDeg.TMJ \cap noTMDs)=0.001 \qquad \qquad \;</math> | ||
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|where | |where | ||
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====Clinical situations==== | ====Clinical situations==== | ||
These conditional probabilities demonstrate that each of the partition's four subclasses is causally relevant to patient data <math>D=\{\delta_1,.....\delta_n\}</math> in the population sample <math>PO</math>. Given the aforementioned partition of the reference class, we have the following clinical situations: | These conditional probabilities demonstrate that each of the partition's four subclasses is causally relevant to patient data <math>D=\{\delta_1,.....\delta_n\}</math> in the population sample <math>PO</math>. Given the aforementioned partition of the reference class, we have the following clinical situations: | ||
*Mary Poppins <math>\in</math> degeneration of the temporomandibular joint <math>\cap</math> Temporomandibular Disorders | *Mary Poppins <math>\in</math> degeneration of the temporomandibular joint <math>\cap</math> Temporomandibular Disorders | ||
*Mary Poppins <math>\in</math> degeneration of the temporomandibular joint <math>\cap</math> no Temporomandibular Disorders | *Mary Poppins <math>\in</math> degeneration of the temporomandibular joint <math>\cap</math> no Temporomandibular Disorders | ||
*Mary Poppins <math>\in</math> no degeneration of the temporomandibular joint <math>\cap</math> Temporomandibular Disorders | *Mary Poppins <math>\in</math> no degeneration of the temporomandibular joint <math>\cap</math> Temporomandibular Disorders | ||
*Mary Poppins <math>\in</math> no degeneration of the temporomandibular joint <math>\cap</math> no Temporomandibular Disorders | *Mary Poppins <math>\in</math> no degeneration of the temporomandibular joint <math>\cap</math> no Temporomandibular Disorders | ||
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---- | ---- | ||
==Final considerations== | ==Final considerations == | ||
We took a long and tortuous path to better understand the complexity encountered by the colleague struggling with the very heavy ethical responsibility of making a diagnosis. However, this task becomes even more complex when we need to be detailed and careful in making a differential diagnosis. | We took a long and tortuous path to better understand the complexity encountered by the colleague struggling with the very heavy ethical responsibility of making a diagnosis. However, this task becomes even more complex when we need to be detailed and careful in making a differential diagnosis. | ||
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| LCCN = | | LCCN = | ||
| OCLC = | | OCLC = | ||
}}</ref> for an implementation in the analysis and reconstruction of how 'knowledge' is built in different disciplines, demands an answer to the following question from the dentist:{{q4|... | }}</ref> for an implementation in the analysis and reconstruction of how 'knowledge' is built in different disciplines, demands an answer to the following question from the dentist: | ||
{{q4|...is there another world or context, parallel to yours, in which in addition to the D data there are further data unknown to you?|}} | |||
and increase the dose: submit Mary Poppins to the following trigeminal electrophysiological tests, label them as we did previously for the set data <math>D=\{\delta_1,\dots\delta_n\}</math> generating another set containing a number <math>m</math> of unknown data (not belonging to the purely dental branch) <math>C=\{\gamma_1,\dots\gamma_m\}</math> thereby creating an entirely new set that we will call <math>S_{unknow}= D+C=\{\delta_1,\dots,\delta_n,\gamma_1,\dots,\gamma_m\}</math> (called <math>S_{unknown}</math> precisely due to the presence of data unknown to the dental context). | and increase the dose: submit Mary Poppins to the following trigeminal electrophysiological tests, label them as we did previously for the set data <math>D=\{\delta_1,\dots\delta_n\}</math> generating another set containing a number <math>m</math> of unknown data (not belonging to the purely dental branch) <math>C=\{\gamma_1,\dots\gamma_m\}</math> thereby creating an entirely new set that we will call <math>S_{unknow}= D+C=\{\delta_1,\dots,\delta_n,\gamma_1,\dots,\gamma_m\}</math> (called <math>S_{unknown}</math> precisely due to the presence of data unknown to the dental context). | ||